Distributed adaptive control for nonlinear multi-agent systems with nonlinear parametric uncertainties

This paper considers the distributed tracking control problem for a class of nonlinear multi-agent systems with nonlinearly parameterized control coefficients and inherent nonlinearities. The essential of multi-agent systems makes it difficult to directly generalize the existing works for single nonlinearly parameterized systems with uncontrollable unstable linearization to the case in this paper. To dominate the inherent nonlinearities and nonlinear parametric uncertainties, a powerful distributed adaptive tracking control is presented by combing the algebra graph theory with the distributed backstepping method, which guarantees that all the closed-loop system signals are global bounded while the range of the tracking error between the follower's output and the leader's output can be tuned arbitrarily small. Finally, a numerical example is provided to verify the validity of the developed methods

Old chest (Karabitsu 唐櫃) of nanatsudera temple(七寺)

The old chest (Karabitsu 唐櫃) of Nanatsudera temple(七寺) was made in 1175. There are 33 boxes. Currently there are 28 boxes in the temple, the other 6 boxes have been deposited at the museum.Non UBCUnreviewedAuthor Affiliation: International College for Postgraduate Buddhist StudiesGraduat

Vision Transformer With Contrastive Learning for Remote Sensing Image Scene Classification

Remote sensing images (RSIs) are characterized by complex spatial layouts and ground object structures. ViT can be a good choice for scene classification owing to the ability to capture long-range interactive information between patches of input images. However, due to the lack of some inductive biases inherent to CNNs, such as locality and translation equivariance, ViT cannot generalize well when trained on insufficient amounts of data. Compared with training ViT from scratch, transferring a large-scale pretrained one is more cost-efficient with better performance even when the target data are small scale. In addition, the cross-entropy (CE) loss is frequently utilized in scene classification yet has low robustness to noise labels and poor generalization performances for different scenes. In this article, a ViT-based model in combination with supervised contrastive learning (CL) is proposed, named ViT-CL. For CL, supervised contrastive (SupCon) loss, which is developed by extending the self-supervised contrastive approach to the fully supervised setting, can explore the label information of RSIs in embedding space and improve the robustness to common image corruption. In ViT-CL, a joint loss function that combines CE loss and SupCon loss is developed to prompt the model to learn more discriminative features. Also, a two-stage optimization framework is introduced to enhance the controllability of the optimization process of the ViT-CL model. Extensive experiments on the AID, NWPU-RESISC45, and UCM datasets verified the superior performance of ViT-CL, with the highest accuracies of 97.42%, 94.54%, and 99.76% among all competing methods, respectively

Increasing the Grain Yield and Grain Protein Content of Common Wheat (Triticum aestivum) by Introducing Missense Mutations in the Q Gene

Grain yield (GY) and grain protein content (GPC) are important traits for wheat breeding and production; however, they are usually negatively correlated. The Q gene is the most important domestication gene in cultivated wheat because it influences many traits, including GY and GPC. Allelic variations in the Q gene may positively affect both GY and GPC. Accordingly, we characterized two new Q alleles (Qs1 and Qc1-N8) obtained through ethyl methanesulfonate-induced mutagenesis. Compared with the wild-type Q allele, Qs1 contains a missense mutation in the sequence encoding the first AP2 domain, whereas Qc1-N8 has two missense mutations: one in the sequence encoding the second AP2 domain and the other in the microRNA172-binding site. The Qs1 allele did not significantly affect GPC or other processing quality parameters, but it adversely affected GY by decreasing the thousand kernel weight and grain number per spike. In contrast, Qc1-N8 positively affected GPC and GY by increasing the thousand kernel weight and grain number per spike. Thus, we generated novel germplasm relevant for wheat breeding. A specific molecular marker was developed to facilitate the use of the Qc1-N8 allele in breeding. Furthermore, our findings provide useful new information for enhancing cereal crops via non-transgenic approaches